Optimized color models for high-quality 3D scanning

We consider the problem of estimating high-quality color models of 3D meshes, given a collection of RGB images of the original object. Applications of a database of high-quality colored meshes include object recognition in robot vision, virtual reality, graphics, and online shopping. Most modern approaches that color a 3D object model from a collection of RGB images face problems in (1) producing realistic colors for non-Lambertian surfaces and (2) seamlessly integrating colors from multiple views. Our approach efficiently solves a non-linear least squares optimization problem to jointly estimate the RGB camera poses and color model. We discover that incorporating 2D texture cues, vertex color smoothing, and texture-adaptive camera viewpoint selection into the optimization problem produces qualitatively more coherent color models than those produced by competing methods. We further introduce practical strategies to accelerate optimization. We provide extensive empirical results on the BigBIRD dataset [15], [21]: results from a user study with 133 participants indicate that on all 16 objects considered, our method outperforms competing approaches. Our code is available for download online at http://rll.berkeley.edu/iros2015colormodels.